Overview

Dataset statistics

Number of variables13
Number of observations19302
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.9 MiB
Average record size in memory104.0 B

Variable types

Categorical2
Numeric11

Dataset

DescriptionThis dataset is about to credit card fraud and which include around 21 feature and 1 target.
URL

Alerts

amt is highly correlated with is_fraudHigh correlation
zip is highly correlated with long and 1 other fieldsHigh correlation
lat is highly correlated with merch_latHigh correlation
long is highly correlated with zip and 1 other fieldsHigh correlation
merch_lat is highly correlated with latHigh correlation
merch_long is highly correlated with zip and 1 other fieldsHigh correlation
is_fraud is highly correlated with amtHigh correlation
amt is highly correlated with is_fraudHigh correlation
zip is highly correlated with long and 1 other fieldsHigh correlation
lat is highly correlated with merch_latHigh correlation
long is highly correlated with zip and 1 other fieldsHigh correlation
merch_lat is highly correlated with latHigh correlation
merch_long is highly correlated with zip and 1 other fieldsHigh correlation
is_fraud is highly correlated with amtHigh correlation
zip is highly correlated with long and 1 other fieldsHigh correlation
lat is highly correlated with merch_latHigh correlation
long is highly correlated with zip and 1 other fieldsHigh correlation
merch_lat is highly correlated with latHigh correlation
merch_long is highly correlated with zip and 1 other fieldsHigh correlation
category is highly correlated with amt and 2 other fieldsHigh correlation
amt is highly correlated with categoryHigh correlation
zip is highly correlated with lat and 3 other fieldsHigh correlation
lat is highly correlated with zip and 3 other fieldsHigh correlation
long is highly correlated with zip and 3 other fieldsHigh correlation
merch_lat is highly correlated with zip and 3 other fieldsHigh correlation
merch_long is highly correlated with zip and 3 other fieldsHigh correlation
hour is highly correlated with category and 1 other fieldsHigh correlation
is_fraud is highly correlated with category and 1 other fieldsHigh correlation
is_fraud is uniformly distributed Uniform
hour has 1143 (5.9%) zeros Zeros
day has 3446 (17.9%) zeros Zeros

Reproduction

Analysis started2023-02-02 02:50:00.141681
Analysis finished2023-02-02 02:50:13.862423
Duration13.72 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

category
Categorical

HIGH CORRELATION

Distinct14
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size150.9 KiB
grocery_pos
3180 
shopping_net
2922 
shopping_pos
1940 
gas_transport
1752 
misc_net
1605 
Other values (9)
7903 

Length

Max length14
Median length11
Mean length10.68687183
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowgrocery_pos
2nd rowgas_transport
3rd rowgrocery_pos
4th rowgas_transport
5th rowgrocery_pos

Common Values

ValueCountFrequency (%)
grocery_pos3180
16.5%
shopping_net2922
15.1%
shopping_pos1940
10.1%
gas_transport1752
9.1%
misc_net1605
8.3%
home1185
 
6.1%
kids_pets1159
 
6.0%
entertainment967
 
5.0%
misc_pos955
 
4.9%
personal_care937
 
4.9%
Other values (4)2700
14.0%

Length

2023-02-02T02:50:13.933532image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
grocery_pos3180
16.5%
shopping_net2922
15.1%
shopping_pos1940
10.1%
gas_transport1752
9.1%
misc_net1605
8.3%
home1185
 
6.1%
kids_pets1159
 
6.0%
entertainment967
 
5.0%
misc_pos955
 
4.9%
personal_care937
 
4.9%
Other values (4)2700
14.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

amt
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct13938
Distinct (%)72.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean299.4012859
Minimum1
Maximum7508.46
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size150.9 KiB
2023-02-02T02:50:14.023906image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4.09
Q120.35
median88.015
Q3479.595
95-th percentile1027.338
Maximum7508.46
Range7507.46
Interquartile range (IQR)459.245

Descriptive statistics

Standard deviation375.6726194
Coefficient of variation (CV)1.254746179
Kurtosis8.056669195
Mean299.4012859
Median Absolute Deviation (MAD)81.165
Skewness1.554209483
Sum5779043.62
Variance141129.917
MonotonicityNot monotonic
2023-02-02T02:50:14.132118image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9.9411
 
0.1%
8.2911
 
0.1%
911
 
0.1%
9.0211
 
0.1%
9.9510
 
0.1%
8.7310
 
0.1%
1.1210
 
0.1%
4.929
 
< 0.1%
7.239
 
< 0.1%
9.159
 
< 0.1%
Other values (13928)19201
99.5%
ValueCountFrequency (%)
13
< 0.1%
1.016
< 0.1%
1.025
< 0.1%
1.034
< 0.1%
1.056
< 0.1%
1.064
< 0.1%
1.073
< 0.1%
1.084
< 0.1%
1.091
 
< 0.1%
1.12
 
< 0.1%
ValueCountFrequency (%)
7508.461
< 0.1%
4673.391
< 0.1%
3304.441
< 0.1%
3066.611
< 0.1%
2967.921
< 0.1%
2717.691
< 0.1%
2162.21
< 0.1%
2105.361
< 0.1%
2025.151
< 0.1%
1866.151
< 0.1%

zip
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct985
Distinct (%)5.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean48786.68179
Minimum1257
Maximum99921
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size150.9 KiB
2023-02-02T02:50:14.235319image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1257
5-th percentile7208
Q126041
median48043
Q372011
95-th percentile95453
Maximum99921
Range98664
Interquartile range (IQR)45970

Descriptive statistics

Standard deviation27049.76004
Coefficient of variation (CV)0.5544496785
Kurtosis-1.083789183
Mean48786.68179
Median Absolute Deviation (MAD)22994
Skewness0.100886626
Sum941680532
Variance731689518
MonotonicityNot monotonic
2023-02-02T02:50:14.338481image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1603452
 
0.3%
7375451
 
0.3%
4808851
 
0.3%
6145449
 
0.3%
6226244
 
0.2%
9916043
 
0.2%
5944843
 
0.2%
8251442
 
0.2%
1685842
 
0.2%
9120642
 
0.2%
Other values (975)18843
97.6%
ValueCountFrequency (%)
125719
0.1%
133019
0.1%
153513
 
0.1%
154515
0.1%
161214
0.1%
184334
0.2%
184430
0.2%
218014
0.1%
263029
0.2%
290817
0.1%
ValueCountFrequency (%)
9992114
 
0.1%
9978322
0.1%
9974712
 
0.1%
9974614
 
0.1%
9932323
0.1%
9916043
0.2%
9911615
 
0.1%
9911314
 
0.1%
9903337
0.2%
9883616
 
0.1%

lat
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct983
Distinct (%)5.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.61214043
Minimum20.0271
Maximum66.6933
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size150.9 KiB
2023-02-02T02:50:14.437907image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum20.0271
5-th percentile29.9912
Q134.7789
median39.39
Q342.0158
95-th percentile45.8433
Maximum66.6933
Range46.6662
Interquartile range (IQR)7.2369

Descriptive statistics

Standard deviation5.126584113
Coefficient of variation (CV)0.1327713008
Kurtosis1.468351282
Mean38.61214043
Median Absolute Deviation (MAD)3.3343
Skewness-0.04440826191
Sum745291.5345
Variance26.28186466
MonotonicityNot monotonic
2023-02-02T02:50:14.542816image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40.855552
 
0.3%
42.516451
 
0.3%
36.38551
 
0.3%
40.676149
 
0.3%
38.931144
 
0.2%
48.887843
 
0.2%
48.277743
 
0.2%
34.155642
 
0.2%
26.421542
 
0.2%
41.000142
 
0.2%
Other values (973)18843
97.6%
ValueCountFrequency (%)
20.027119
0.1%
20.082722
0.1%
24.655729
0.2%
26.118441
0.2%
26.330415
 
0.1%
26.377110
 
0.1%
26.421542
0.2%
26.472237
0.2%
26.52920
0.1%
26.693916
 
0.1%
ValueCountFrequency (%)
66.693312
 
0.1%
65.689914
 
0.1%
64.755622
0.1%
55.473214
 
0.1%
48.887843
0.2%
48.885622
0.1%
48.832829
0.2%
48.666921
0.1%
48.603122
0.1%
48.478626
0.1%

long
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct983
Distinct (%)5.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-90.3445627
Minimum-165.6723
Maximum-67.9503
Zeros0
Zeros (%)0.0%
Negative19302
Negative (%)100.0%
Memory size150.9 KiB
2023-02-02T02:50:14.647586image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-165.6723
5-th percentile-120.1922
Q1-96.8094
median-87.5917
Q3-80.1629
95-th percentile-73.3113
Maximum-67.9503
Range97.722
Interquartile range (IQR)16.6465

Descriptive statistics

Standard deviation14.09176155
Coefficient of variation (CV)-0.1559779707
Kurtosis1.958082009
Mean-90.3445627
Median Absolute Deviation (MAD)8.2675
Skewness-1.191739657
Sum-1743830.749
Variance198.5777437
MonotonicityNot monotonic
2023-02-02T02:50:14.864050image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-79.737252
 
0.3%
-98.072751
 
0.3%
-82.983251
 
0.3%
-91.039149
 
0.3%
-89.246344
 
0.2%
-118.210543
 
0.2%
-112.845643
 
0.2%
-99.002542
 
0.2%
-73.09842
 
0.2%
-79.785642
 
0.2%
Other values (973)18843
97.6%
ValueCountFrequency (%)
-165.672322
0.1%
-156.29214
0.1%
-155.48822
0.1%
-155.369719
0.1%
-153.99412
0.1%
-133.117114
0.1%
-124.440922
0.1%
-124.217426
0.1%
-124.158717
0.1%
-124.143727
0.1%
ValueCountFrequency (%)
-67.950331
0.2%
-68.556519
0.1%
-69.267514
 
0.1%
-69.482826
0.1%
-69.957610
 
0.1%
-69.965638
0.2%
-70.10319
 
< 0.1%
-70.23910
 
0.1%
-70.300129
0.2%
-70.345732
0.2%

city_pop
Real number (ℝ≥0)

Distinct891
Distinct (%)4.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean88944.23977
Minimum23
Maximum2906700
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size150.9 KiB
2023-02-02T02:50:14.966896image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum23
5-th percentile140
Q1760
median2526
Q319685
95-th percentile525713
Maximum2906700
Range2906677
Interquartile range (IQR)18925

Descriptive statistics

Standard deviation301863.0315
Coefficient of variation (CV)3.393845766
Kurtosis38.23944993
Mean88944.23977
Median Absolute Deviation (MAD)2263
Skewness5.628461696
Sum1716801716
Variance9.112128981 × 1010
MonotonicityNot monotonic
2023-02-02T02:50:15.070920image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60675
 
0.4%
126332167
 
0.3%
30265
 
0.3%
290670065
 
0.3%
159579761
 
0.3%
60172359
 
0.3%
112658
 
0.3%
131292258
 
0.3%
157738557
 
0.3%
47157
 
0.3%
Other values (881)18680
96.8%
ValueCountFrequency (%)
2319
0.1%
3713
 
0.1%
439
 
< 0.1%
4637
0.2%
479
 
< 0.1%
4924
0.1%
5123
0.1%
5217
0.1%
5338
0.2%
6022
0.1%
ValueCountFrequency (%)
290670065
0.3%
250470025
 
0.1%
238391213
 
0.1%
159579761
0.3%
157738557
0.3%
152620639
0.2%
14177938
 
< 0.1%
138248019
 
0.1%
131292258
0.3%
126332167
0.3%

merch_lat
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct19295
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.60800665
Minimum19.161782
Maximum67.510267
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size150.9 KiB
2023-02-02T02:50:15.175744image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum19.161782
5-th percentile29.8023046
Q134.880532
median39.4115625
Q341.9913195
95-th percentile46.0132691
Maximum67.510267
Range48.348485
Interquartile range (IQR)7.1107875

Descriptive statistics

Standard deviation5.164621843
Coefficient of variation (CV)0.1337707458
Kurtosis1.447753867
Mean38.60800665
Median Absolute Deviation (MAD)3.347456
Skewness-0.04791012658
Sum745211.7444
Variance26.67331879
MonotonicityNot monotonic
2023-02-02T02:50:15.274816image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
38.4015612
 
< 0.1%
40.8767632
 
< 0.1%
43.0674862
 
< 0.1%
41.2220942
 
< 0.1%
40.9311092
 
< 0.1%
33.0290362
 
< 0.1%
40.4112812
 
< 0.1%
41.3197531
 
< 0.1%
38.9454371
 
< 0.1%
48.4314511
 
< 0.1%
Other values (19285)19285
99.9%
ValueCountFrequency (%)
19.1617821
< 0.1%
19.2153181
< 0.1%
19.2382691
< 0.1%
19.3138941
< 0.1%
19.3939221
< 0.1%
19.3992061
< 0.1%
19.4251141
< 0.1%
19.5311441
< 0.1%
19.6070921
< 0.1%
19.6088861
< 0.1%
ValueCountFrequency (%)
67.5102671
< 0.1%
67.4415181
< 0.1%
67.3970181
< 0.1%
67.1881111
< 0.1%
67.0642771
< 0.1%
66.8351741
< 0.1%
66.6054451
< 0.1%
66.5915651
< 0.1%
66.4106511
< 0.1%
66.3572151
< 0.1%

merch_long
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct19295
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-90.34688463
Minimum-166.558056
Maximum-66.960745
Zeros0
Zeros (%)0.0%
Negative19302
Negative (%)100.0%
Memory size150.9 KiB
2023-02-02T02:50:15.376394image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-166.558056
5-th percentile-120.125384
Q1-96.9514715
median-87.493075
Q3-80.237604
95-th percentile-73.2030188
Maximum-66.960745
Range99.597311
Interquartile range (IQR)16.7138675

Descriptive statistics

Standard deviation14.10718771
Coefficient of variation (CV)-0.1561447056
Kurtosis1.95989688
Mean-90.34688463
Median Absolute Deviation (MAD)8.287123
Skewness-1.189822007
Sum-1743875.567
Variance199.012745
MonotonicityNot monotonic
2023-02-02T02:50:15.480447image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-77.8079722
 
< 0.1%
-82.3336582
 
< 0.1%
-122.6580932
 
< 0.1%
-81.6590162
 
< 0.1%
-82.9767782
 
< 0.1%
-96.7379232
 
< 0.1%
-76.9268582
 
< 0.1%
-96.3541571
 
< 0.1%
-87.4206521
 
< 0.1%
-93.8845431
 
< 0.1%
Other values (19285)19285
99.9%
ValueCountFrequency (%)
-166.5580561
< 0.1%
-166.5507791
< 0.1%
-166.4787341
< 0.1%
-166.4039731
< 0.1%
-166.1630251
< 0.1%
-166.1070631
< 0.1%
-166.0802071
< 0.1%
-166.0670291
< 0.1%
-165.9861171
< 0.1%
-165.9145421
< 0.1%
ValueCountFrequency (%)
-66.9607451
< 0.1%
-67.1541411
< 0.1%
-67.389031
< 0.1%
-67.3924891
< 0.1%
-67.3947111
< 0.1%
-67.4941181
< 0.1%
-67.5032511
< 0.1%
-67.5335811
< 0.1%
-67.5692381
< 0.1%
-67.6185471
< 0.1%

age
Real number (ℝ≥0)

Distinct83
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47.56196249
Minimum14
Maximum96
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size150.9 KiB
2023-02-02T02:50:15.581137image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum14
5-th percentile22
Q133
median46
Q359
95-th percentile82
Maximum96
Range82
Interquartile range (IQR)26

Descriptive statistics

Standard deviation18.03992112
Coefficient of variation (CV)0.3792930354
Kurtosis-0.4138004026
Mean47.56196249
Median Absolute Deviation (MAD)13
Skewness0.5003906678
Sum918041
Variance325.4387541
MonotonicityNot monotonic
2023-02-02T02:50:15.681123image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
33513
 
2.7%
47503
 
2.6%
34496
 
2.6%
35481
 
2.5%
48480
 
2.5%
32476
 
2.5%
46454
 
2.4%
43442
 
2.3%
30437
 
2.3%
49412
 
2.1%
Other values (73)14608
75.7%
ValueCountFrequency (%)
1410
 
0.1%
1537
 
0.2%
1697
 
0.5%
1715
 
0.1%
1886
 
0.4%
1994
 
0.5%
20220
1.1%
21227
1.2%
22365
1.9%
23314
1.6%
ValueCountFrequency (%)
967
 
< 0.1%
951
 
< 0.1%
9460
0.3%
9366
0.3%
92104
0.5%
9173
0.4%
9080
0.4%
8979
0.4%
8850
0.3%
8772
0.4%

hour
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct24
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.39954409
Minimum0
Maximum23
Zeros1143
Zeros (%)5.9%
Negative0
Negative (%)0.0%
Memory size150.9 KiB
2023-02-02T02:50:15.771271image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14
median15
Q322
95-th percentile23
Maximum23
Range23
Interquartile range (IQR)18

Descriptive statistics

Standard deviation8.405531312
Coefficient of variation (CV)0.6272997989
Kurtosis-1.459755638
Mean13.39954409
Median Absolute Deviation (MAD)7
Skewness-0.319960327
Sum258638
Variance70.65295663
MonotonicityNot monotonic
2023-02-02T02:50:15.854049image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
232953
15.3%
222939
15.2%
11156
 
6.0%
01143
 
5.9%
31134
 
5.9%
21106
 
5.7%
16607
 
3.1%
19599
 
3.1%
13595
 
3.1%
18595
 
3.1%
Other values (14)6475
33.5%
ValueCountFrequency (%)
01143
5.9%
11156
6.0%
21106
5.7%
31134
5.9%
4386
 
2.0%
5375
 
1.9%
6367
 
1.9%
7378
 
2.0%
8356
 
1.8%
9409
 
2.1%
ValueCountFrequency (%)
232953
15.3%
222939
15.2%
21574
 
3.0%
20571
 
3.0%
19599
 
3.1%
18595
 
3.1%
17586
 
3.0%
16607
 
3.1%
15594
 
3.1%
14579
 
3.0%

day
Real number (ℝ≥0)

ZEROS

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.023779919
Minimum0
Maximum6
Zeros3446
Zeros (%)17.9%
Negative0
Negative (%)0.0%
Memory size150.9 KiB
2023-02-02T02:50:15.929503image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q35
95-th percentile6
Maximum6
Range6
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.134026467
Coefficient of variation (CV)0.7057479459
Kurtosis-1.390979879
Mean3.023779919
Median Absolute Deviation (MAD)2
Skewness-0.03792912189
Sum58365
Variance4.554068961
MonotonicityNot monotonic
2023-02-02T02:50:15.999087image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
03446
17.9%
63351
17.4%
52848
14.8%
12685
13.9%
42511
13.0%
32368
12.3%
22093
10.8%
ValueCountFrequency (%)
03446
17.9%
12685
13.9%
22093
10.8%
32368
12.3%
42511
13.0%
52848
14.8%
63351
17.4%
ValueCountFrequency (%)
63351
17.4%
52848
14.8%
42511
13.0%
32368
12.3%
22093
10.8%
12685
13.9%
03446
17.9%

month
Real number (ℝ≥0)

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.754999482
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size150.9 KiB
2023-02-02T02:50:16.074812image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median7
Q310
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.477986257
Coefficient of variation (CV)0.5148758732
Kurtosis-1.196016523
Mean6.754999482
Median Absolute Deviation (MAD)3
Skewness-0.0352673039
Sum130385
Variance12.0963884
MonotonicityNot monotonic
2023-02-02T02:50:16.146203image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
122313
12.0%
61741
9.0%
81694
8.8%
51674
8.7%
31664
8.6%
71563
8.1%
101547
8.0%
91513
7.8%
41416
7.3%
11413
7.3%
Other values (2)2764
14.3%
ValueCountFrequency (%)
11413
7.3%
21360
7.0%
31664
8.6%
41416
7.3%
51674
8.7%
61741
9.0%
71563
8.1%
81694
8.8%
91513
7.8%
101547
8.0%
ValueCountFrequency (%)
122313
12.0%
111404
7.3%
101547
8.0%
91513
7.8%
81694
8.8%
71563
8.1%
61741
9.0%
51674
8.7%
41416
7.3%
31664
8.6%

is_fraud
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIFORM

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size150.9 KiB
1
9651 
0
9651 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
19651
50.0%
09651
50.0%

Length

2023-02-02T02:50:16.234581image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2023-02-02T02:50:16.295563image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
09651
50.0%
19651
50.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Interactions

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2023-02-02T02:50:12.151584image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Correlations

2023-02-02T02:50:16.353523image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2023-02-02T02:50:16.500404image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2023-02-02T02:50:16.770004image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2023-02-02T02:50:16.902498image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2023-02-02T02:50:17.006992image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2023-02-02T02:50:13.433502image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
A simple visualization of nullity by column.
2023-02-02T02:50:13.761121image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

categoryamtziplatlongcity_popmerch_latmerch_longagehourdaymonthis_fraud
0grocery_pos281.062861135.9946-81.726688536.430124-81.179483311211
1gas_transport11.527820829.4400-98.4590159579729.819364-99.142791591211
2grocery_pos276.317820829.4400-98.4590159579729.273085-98.836360593211
3gas_transport7.032861135.9946-81.726688535.909292-82.091010313211
4grocery_pos275.737820829.4400-98.4590159579729.786426-98.683410593211
5shopping_net844.802861135.9946-81.726688535.987802-81.2543323113211
6misc_net843.912861135.9946-81.726688535.985612-81.3833063123211
7gas_transport10.767820829.4400-98.4590159579728.856712-97.794207591311
8grocery_pos332.357820829.4400-98.4590159579729.320662-97.937219591311
9grocery_pos315.347820829.4400-98.4590159579728.953283-97.806528593311

Last rows

categoryamtziplatlongcity_popmerch_latmerch_longagehourdaymonthis_fraud
19292personal_care86.537375436.3850-98.0727107836.665223-98.2473176812450
19293health_fitness66.643892233.9215-89.6782345133.417267-89.4035803623060
19294gas_transport80.624808842.5164-82.983213405641.852968-83.2732486311530
19295gas_transport48.321705840.5553-77.4001190940.264776-77.3176576602120
19296misc_net74.774634641.4802-86.6919142341.023610-87.02894222126120
19297gas_transport62.582945532.8357-79.82172047832.418947-78.8736602210320
19298misc_net273.251471142.3200-78.0943176641.831789-77.1511605883100
19299misc_pos2.745312942.9373-87.99431397342.604173-88.5733573874120
19300shopping_net161.284007738.4921-85.452456438.225422-85.13504523161120
19301kids_pets163.684423341.2419-81.7453764641.654965-80.8115803122430